Pallets are widely used for goods handling and transportation. Typically, the pallets are repositioned by manned or unmanned vehicles (SGVs), for example by manned or unmanned fork lifts. The unmanned vehicles are called auto-guided vehicles (AGVs) or self-guided vehicles (SGVs) (hereinafter, collectively referred to as SGVs). To perform a pallet pick operation, an SGV must know the exact location and orientation of the pallet. In a typical warehouse, an approximate pallet location is usually known, and it can be obtained from a database or an inventory management system. Therefore, the SGV can use the known location of the pallet to drive toward the pallet. However, to perform a pick operation, the SGV must align against the pallet and must insert the forks inside the pallet pockets. To do this, the SGV needs to know an accurate pallet pose relative to itself.
With some conventional technologies, the pallets are stored in the racks. The racks physically constrain the pallet to a known location and orientation. To pick the pallet, an SGV can exploit this knowledge and blindly orient itself assuming that the expected pallet is where it is supposed to be. To verify a successful insertion of the forks, a bump sensor can be used to detect the front face of the pallet engaging the back of the forks.
With some conventional technologies, a laser point ranging sensor is mounted near the forks. The laser point sensor emits a light beam which is used to measure the distance to a target object. If mounted next to the forks, these sensors can be used to check whether the forks are going to clear the pockets.
With some conventional technologies, a 2D laser scanner emits laser beams in a plane. These 2D laser beams can be used to identify the locations of the pallet pockets. Furthermore, sonar sensors can be used to determine whether or not the forks can clear the pockets. Sonar sensors emit ultrasonic sound waves, and can measure time required for the return trip of the ultrasonic sound waves, thus determining a distance to the pockets.
With some other technologies, a 2D camera or a stereo camera can obtain an image of the pallet. Next, image processing algorithms can be used to identify the pallet pockets based on edge detection, template matching, or color matching.
However, the conventional technologies may be imprecise in some situations, especially when the true orientation of the pallet is not known. Furthermore, some conventional technologies require a significant computational effort. Therefore, a need remains for pallet detection technologies that produce accurate location of the pallet.